Modellizzazione e analisi del metabolismo energetico del cervello. Applicazioni alle lastre mediche del glioma diffuso di basso grado

Tout ce qui vit, naît, se nourrit, se reproduit et meurt. Pour le cerveau, la question se complexifie car à la survie des neurones s'ajoute le coût de l'activité cérébrale. La question de la gestion énergétique pour les neurones est particulière car les cellules de notre cerveau évoluent de manière concertée et non par compétition. On sait avec l'imagerie médicale que l'usine neuronale ne fonctionne pas uniquement grâce au glucose ; elle utilise d'autres apports énergétiques tels que le lactate ou le glutamate pour soutenir sa production. Lorsqu'une tumeur apparaît, elle change le métabolisme énergétique pour survivre et soutenir sa propre croissance. En particulier, les cellules cancéreuses se fournissent en lactate et choisissent leur substrat préféré en fonction de l'oxygène disponible. La modélisation mathématique des substrats énergétiques est un outil de choix pour décrire et prédire de tels flux. Coupler ces modèles à des données issues de l'IRM et de la SRM permet d'améliorer la prise en charge du patient présentant un gliome.Cette thèse propose l'approche de plusieurs dynamiques en substrat dans le cerveau sain et gliomateux en se basant sur des systèmes d'équations : échanges locaux en lactate (EDO, système lent-rapide), échanges globaux en substrats (EDO), cycle glutamate/glutamine (EDR) et échanges en lactate en dimensions supérieures (EDP). Ces modèles sont expliqués, décrits grâce aux mathématiques et permettent l'élaboration de simulations ajustées selon des données patient ou issues de la littérature.L'énergie est nécessaire au maintien de la vie. Mais si votre voisin consomme une partie de vos ressources, pouvez-vous encore espérer survivre ?Everything that lives is born, eats, reproduces and dies. For the brain, the question is more complex because neurons have to survive and to support brain activity. Energy management is also particular because brain cells evolve together with no competition. Thanks to medical imaging, we know that neurons do not consume only glucose. They can use others energetic substrates such as lactate and glutamate as a power source.When a tumor appears, it changes the energetic metabolism to survive and support its own growth. In particular, cancer cells like to consume lactate. They also choose their favorite substrate based on the available oxygen. Modeling of energy substrates is useful to describe and predict energetic kinetics and changes. Mathematical models could get with clinical and medical results to describe, explain or predict low grade glioma dynamics. They can help to characterize and quantify a tumor evolution, then leading to improve their therapeutical management. Exchanges between mathematics and MRI (and MRS) enable to get accurate data and to build suitable mathematical models.This thesis deals with several approaches of substrates dynamics in healthy and gliomatous brains. These researches are based on systems of equations. We model local lactate exchanges (ODE, fast-slow systems), global substrates exchanges (ODE), glutamate/glutamine cycle (RDE) and local lactate exchanges in higher dimensions (PDE). We describe, analyze and give simulations of these models. Simulations are fitted on patient MRI data or literature data. Energy is necessary to live. But if your neighbor consumes a part of your resources, can you still survive ?Tutto ciò che vive nasce, si nutre, si riproduce e muore. Per il cervello, la questione è più complessa perché i neuroni devono sopravvivere e sostenere l'attività cerebrale. La gestione energetica cerebrale è particolare anche perché le cellule cerebrali evolvono insieme, senza concorrenza. Inoltre, grazie alle immagini mediche, sappiamo che i neuroni non consumano solo del glucosio ma usano altri substrati energetici come il lattato o il glutammato.Quando un tumore si stabilisce, cambia il metabolismo energetico del cervello per sopravvivere e sostenere la propria crescita. In particolare, cellule tumorali consumano del lattato e scelgono il loro substrato preferito basandosi all'ossigeno disponibile.La matematica, e in particolare l'elaborazione di modelli matematici può aiutarci a ottimizzare i dati disponibili, che possono essere, di volta in volta, delle proprietà cellulare o delle lastre MRI o MRS. La modellizzazione dei substrati energetici potrebbe descrivere, spiegare o prevedere le dinamiche energetiche nel cervello.Questa tesi tratta di diversi approcci della dinamica dei substrati nei cervelli sani e gliomatosi. Queste ricerche si basano su sistemi di equazioni. Modellizziamo scambi locali di lattato (ODE, sistemi fast-slow), scambi globali di substrati (ODE), ciclo glutammato/glutammina (RDE) e scambi locali di lattato in dimensioni superiori (PDE). Descriviamo, analizziamo e diamo simulazioni di questi modelli. Le simulazioni sono adeguate su dati MRI paziente o dati di letteratura.Per vivere, l’energia è una necessità. Ma se i Suoi vicini consumassero le Sue risorse, riuscirebbe ancora a sopravvivere

Modellizzazione e analisi del metabolismo energetico del cervello. Applicazioni alle lastre mediche del glioma diffuso di basso grado

Everything that lives is born, eats, reproduces and dies. For the brain, the question is more complex because neurons have to survive and to support brain activity. Energy management is also particular because brain cells evolve together with no competition. Thanks to medical imaging, we know that neurons do not consume only glucose. They can use others energetic substrates such as lactate and glutamate as a power source.When a tumor appears, it changes the energetic metabolism to survive and support its own growth. In particular, cancer cells like to consume lactate. They also choose their favorite substrate based on the available oxygen. Modeling of energy substrates is useful to describe and predict energetic kinetics and changes. Mathematical models could get with clinical and medical results to describe, explain or predict low grade glioma dynamics. They can help to characterize and quantify a tumor evolution, then leading to improve their therapeutical management. Exchanges between mathematics and MRI (and MRS) enable to get accurate data and to build suitable mathematical models.This thesis deals with several approaches of substrates dynamics in healthy and gliomatous brains. These researches are based on systems of equations. We model local lactate exchanges (ODE, fast-slow systems), global substrates exchanges (ODE), glutamate/glutamine cycle (RDE) and local lactate exchanges in higher dimensions (PDE). We describe, analyze and give simulations of these models. Simulations are fitted on patient MRI data or literature data. Energy is necessary to live. But if your neighbor consumes a part of your resources, can you still survive ?Tout ce qui vit, naît, se nourrit, se reproduit et meurt. Pour le cerveau, la question se complexifie car à la survie des neurones s'ajoute le coût de l'activité cérébrale. La question de la gestion énergétique pour les neurones est particulière car les cellules de notre cerveau évoluent de manière concertée et non par compétition. On sait avec l'imagerie médicale que l'usine neuronale ne fonctionne pas uniquement grâce au glucose ; elle utilise d'autres apports énergétiques tels que le lactate ou le glutamate pour soutenir sa production. Lorsqu'une tumeur apparaît, elle change le métabolisme énergétique pour survivre et soutenir sa propre croissance. En particulier, les cellules cancéreuses se fournissent en lactate et choisissent leur substrat préféré en fonction de l'oxygène disponible. La modélisation mathématique des substrats énergétiques est un outil de choix pour décrire et prédire de tels flux. Coupler ces modèles à des données issues de l'IRM et de la SRM permet d'améliorer la prise en charge du patient présentant un gliome.Cette thèse propose l'approche de plusieurs dynamiques en substrat dans le cerveau sain et gliomateux en se basant sur des systèmes d'équations : échanges locaux en lactate (EDO, système lent-rapide), échanges globaux en substrats (EDO), cycle glutamate/glutamine (EDR) et échanges en lactate en dimensions supérieures (EDP). Ces modèles sont expliqués, décrits grâce aux mathématiques et permettent l'élaboration de simulations ajustées selon des données patient ou issues de la littérature.L'énergie est nécessaire au maintien de la vie. Mais si votre voisin consomme une partie de vos ressources, pouvez-vous encore espérer survivre ?Tutto ciò che vive nasce, si nutre, si riproduce e muore. Per il cervello, la questione è più complessa perché i neuroni devono sopravvivere e sostenere l'attività cerebrale. La gestione energetica cerebrale è particolare anche perché le cellule cerebrali evolvono insieme, senza concorrenza. Inoltre, grazie alle immagini mediche, sappiamo che i neuroni non consumano solo del glucosio ma usano altri substrati energetici come il lattato o il glutammato.Quando un tumore si stabilisce, cambia il metabolismo energetico del cervello per sopravvivere e sostenere la propria crescita. In particolare, cellule tumorali consumano del lattato e scelgono il loro substrato preferito basandosi all'ossigeno disponibile.La matematica, e in particolare l'elaborazione di modelli matematici può aiutarci a ottimizzare i dati disponibili, che possono essere, di volta in volta, delle proprietà cellulare o delle lastre MRI o MRS. La modellizzazione dei substrati energetici potrebbe descrivere, spiegare o prevedere le dinamiche energetiche nel cervello.Questa tesi tratta di diversi approcci della dinamica dei substrati nei cervelli sani e gliomatosi. Queste ricerche si basano su sistemi di equazioni. Modellizziamo scambi locali di lattato (ODE, sistemi fast-slow), scambi globali di substrati (ODE), ciclo glutammato/glutammina (RDE) e scambi locali di lattato in dimensioni superiori (PDE). Descriviamo, analizziamo e diamo simulazioni di questi modelli. Le simulazioni sono adeguate su dati MRI paziente o dati di letteratura.Per vivere, l’energia è una necessità. Ma se i Suoi vicini consumassero le Sue risorse, riuscirebbe ancora a sopravvivere

Stability analysis of a steady state of a model describing Alzheimer’s disease and interactions with prion proteins

International audienceAlzheimer's disease (AD) is a neuro-degenerative disease affecting more than 46 million people worldwide in 2015. AD is in part caused by the accumulation of Aβ peptides inside the brain. These can aggregate to form insoluble oligomers or fibrils. Oligomers have the capacity to interact with neurons via membrane receptors such as prion proteins (PrP C). This interaction leads PrP C to be misfolded in oligomeric prion proteins (PrP ol), transmitting a death signal to neurons. In this work, we propose a new mathematical model bringing together different mechanisms: Aβ polymerization, including formation of oligomers and fibrils, and interaction between Aβ oligomers and prion proteins. The model is based on Becker-Döring equations for the polymer-ization process, with delayed differential equations accounting for Aβ/PrP C interactions. We analyse the well-posedness of the model and show existence, uniqueness and non-negativity of 2 Helal et al. solutions. Moreover, we demonstrate that this model admits a non-trivial steady state, which is found to be globally stable thanks to a Lyapunov function. We finally present numerical simulations and discuss the impact of model parameters on the whole dynamics, which could constitute the main targets for pharmaceutical industry

Empirical methods for the validation of time-to-event mathematical models taking into account uncertainty and variability: application to EGFR + lung adenocarcinoma

Abstract Background Over the past several decades, metrics have been defined to assess the quality of various types of models and to compare their performance depending on their capacity to explain the variance found in real-life data. However, available validation methods are mostly designed for statistical regressions rather than for mechanistic models. To our knowledge, in the latter case, there are no consensus standards, for instance for the validation of predictions against real-world data given the variability and uncertainty of the data. In this work, we focus on the prediction of time-to-event curves using as an application example a mechanistic model of non-small cell lung cancer. We designed four empirical methods to assess both model performance and reliability of predictions: two methods based on bootstrapped versions of parametric statistical tests: log-rank and combined weighted log-ranks (MaxCombo); and two methods based on bootstrapped prediction intervals, referred to here as raw coverage and the juncture metric. We also introduced the notion of observation time uncertainty to take into consideration the real life delay between the moment when an event happens, and the moment when it is observed and reported. Results We highlight the advantages and disadvantages of these methods according to their application context. We have shown that the context of use of the model has an impact on the model validation process. Thanks to the use of several validation metrics we have highlighted the limit of the model to predict the evolution of the disease in the whole population of mutations at the same time, and that it was more efficient with specific predictions in the target mutation populations. The choice and use of a single metric could have led to an erroneous validation of the model and its context of use. Conclusions With this work, we stress the importance of making judicious choices for a metric, and how using a combination of metrics could be more relevant, with the objective of validating a given model and its predictions within a specific context of use. We also show how the reliability of the results depends both on the metric and on the statistical comparisons, and that the conditions of application and the type of available information need to be taken into account to choose the best validation strategy